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The Importance of Conducting Baseline Surveys Before a Particular Project Has Commenced.

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THE IMPORTANCE OF CONDUCTING BASELINE SURVEYS BEFORE A PARTICULAR PROJECT HAS COMMENCED.

The Baseline Survey is the first step in the project. A Baseline Survey gathers key information early in a project so that later judgments can be made about the quality and development results achieved of the project. The project’s monitoring and evaluation plan is closely linked to each (objective) level of the log frame and includes indicators of achievement and means of verification. The Baseline Survey is an early element in the monitoring and evaluation plan and uses the log frame structure to systematically assess the circumstances in which the project commences. It provides the basis for subsequent assessment of how efficiently the activity …show more content…

In the same way that the determination of variables is critical for the definition of the content of a system, so the number of cases is critical for the definition of the structure of a system. This number is determined by two distinct factors: methodological considerations of sample representativeness and resources available to implement the baseline case studies.
Perfect representativeness would be guaranteed only if every community included in a programme is studied. Statistical sampling basically seeks to select a sample of cases in such a way that it is possible to guarantee in advance that the sample will be representative of the entire population. The rule in sampling is that the degree to which representativeness can be guaranteed depends on the absolute number of cases in the sample A general rule of thumb is that the larger the number of cases in a sample, the greater the guaranteed representativeness. However, this general rule is conditioned by two other factors. First, the degree of representativeness of a sample does not increase proportionately as the size of a sample increases. That is, in an infinite population, assuming that 95 times out of 100 the sample accurately represents the population, a simple random sample of 196 yields a 7 per cent probable error, a sample of 384 yields a 5 per cent probable error, a sample of 600 yields a 14 per cent probable error, a sample of 1,061 yields a 3 per cent probable error and only with 9,604 cases

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